臺灣博碩士論文加值系統

合作式過濾推薦方法是目前最受到廣泛利用的過濾推薦技術之一，傳統的合作式過濾推薦系統主要是利用使用者對於曾經使用的項目給予喜好程度的評等矩陣，計算出使用者彼此之間的相似度，利用同好的評等紀錄，進一步給推薦目標使用者可能喜愛或感興趣的項目。但是若僅僅採用評等紀錄作為相似度計算的依據，資訊稍顯然不足。若能進一步考量使用項目的特徵，以及使用者的使用偏好，應該能提高推薦的準確度。
本研究以 Movielens的資料集為例，提出了一個植基於分群演算法的混合式合作過濾推薦系統，首先依電影的類別特徵和使用者給予的評等矩陣分別加以分群，藉此產生同質性較高的電影群。同樣的，使用者也利用偏好觀賞的電影類別及評等矩陣，計算使用者之間的相似度。在推薦的預測上，我們使用了混合式的方法，除了採用傳統合作式過濾推薦方法的預測方式，同時增加了同質電影群的考量。當系統因為資料集的稀疏性造成資訊不足，導致傳統方法無法有效推薦時，本研究方法仍然能維持穩定且較高準確度的推薦效果。

Collaborative recommender is one of the most popular recommendation techniques. Traditional collaborative filtering approach mainly employs a matrix of user’s ratings on items to calculate the similarity between users. If the features of users or items are provided in the data set in addition to the rating data, then those features can be used to improve the quality of recommendations.
In this thesis, we proposed a hybrid recommender system based on clustering and collaborative filtering techniques. In the proposed system, items are clustered based on item features and user-item rating matrix. Similarly, users are clustered based on the user’s preferred categories of items and user-item rating matrix. Then a hybrid method that combines content-based and collaborative filtering is proposed to predict the rating of an item for a given user. The experimental results show that the proposed method has higher accuracy in terms of mean absolute error than that of User-based collaborative filtering approach, Item-based filtering approach, Clustering Items for Collaborative Filtering (CICF), and the User Profile Clustering (UPC) method. Especially, when the dataset is sparse, the accuracy of the proposed method is better and more stable than the other methods.

摘要 i
Abstract ii
目錄 iii
圖目錄 iv
表目錄 v
第一章 緒論 1
1.1 研究動機與目的 1
1.2 問題描述 2
1.3 主要貢獻 3
1.4 論文架構 4
第二章 相關研究 5
2.1 資訊過濾技術(Information Filtering) 5
2.1.1 內容式過濾技術(Content-Based Filtering) 7
2.1.2 合作式過濾技術(Collaborative Filtering) 8
2.2 分群技術 12
2.3 限定範疇 13
2.4 使用分群概念的合作式過濾 14
2.5 稀疏資料的處理 16
第三章 系統架構與演算法 18
3.1 同質電影的評估 20
3.1.1 以電影類別特徵分群 20
3.1.2 以使用者評等資料分群 22
3.1.3 同質電影 23
3.2 使用者相似度評估 25
3.3 使用者對電影評等的預測 27
第四章 系統實作與實驗分析 31
4.1 實驗測試環境與工具 31
4.2 實驗資料集 31
4.3 推薦系統使用範例 32
4.4 準確度的評估 35
4.5 實驗結果評估 37
4.6 推薦效能評估 40
第五章 結論與未來研究方向 42
參考文獻 43

[1]廖宜恩、許雯絞、鄭名珊，「植基於個人本體論與合作式過濾的英文館藏推薦系統」，2008 數位生活科技研討會，成功大學，June 5-6, 2008.
[2]Lyle H. Ungar and Dean P. Foster, “Clustering Methods for Collaborative Filtering”, AAAI Workshop on Recommendation Systems, 1998.
[3]Uri Handani, Bracha Shapira and Peretz Shoval, “Information Filtering : Overview of Issues, Research and Systems”, User Modeling and User-Adapted Interaction, 11, pp. 203-259,2001.
[4]Jacob Palme, “Information Filtering”, Department of Computer and Systems Sciences, 1998.
[5]N. J. Belkin and W. B. Croft, “Information filtering and information retrieval: two sides of the same coin?”, Communications of the ACM, vol. 35, Issue 12, Special issue on information filtering, December 1992.
[6]Robin van Meteren and Maarten van Someren, “Using content-based filtering for recommendation”, Proceedings of ECML Workshop: Machine Learning in New Information, 2000.
[7]Y. Koren, “Tutorial on Recent progress in Collaborative Filtering,” RecSys’08, Lausanne, Switzerland, 2008.
[8]D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using Collaborative Filtering to Weave and Information Tapestry”, Communications of the ACM, Vol. 35, Issue 12, December 1992, pp.61-70.
[9]Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl, “Explaining Collaborative Filtering Recommendations”, CSCW’00, Philadelphia, PA.December 2-6, 2000,
[10]B. Sawar, G. Karypis, J. Konstan, and j. Riedl, “Item-based Collaborative Filtering Recommendation Algorithms”, Proceedings of the 10th International World Wide Web Conference, Hong Kong, May 1-5, 2001, pp. 285-295.
[11]B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based Collaborative Filtering Recommendation Algorithms” in Proc. IEEE Internet Computing, 10th International World Wide Web Conference, 2001.
[12]Linden, G., Smith, B., York, J., “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”, in IEEE Internet Computing, vol. 7(1), 2003.
[13]Salton, G., & McGill, M. J., Introduction to Modern Information Retrieval. N.Y.: McGraw Hill Book Company, 1983.
[14]Ricardo Baeza-Yates, Berthier Ribeiro-Neto, “Modern information retrieval”, Harlow, England ;Addison-Wesley Longman, 1999.
[15]Miller, B.N., Konstan, J.A., Riedl, J., “PocketLens: Toward a Personal Recommender System”, in ACM TOIS, vol.22 (3), 2004
[16]Yu Li, Liu Lu, Li Xuefeng, A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce, Expert Systems with Applications 28, 2005.
[17]Herlocker, J. L., Konstan,J. A.,Terveen, L.G., and Riedl, J.T.,”Evaluating Collaborative Filtering Recommender Systems”, ACM Transactions on information System (TOIS), Vol.22, Issue 1, pp.5-53,2004.
[18]Y. Zhao and G. Karypis., “Hierarchical clustering algorithms for document datasets.”, Data Mining and Knowledge Discovery, 10:141-168, 2005.
[19]Berkvosky, S., Kuflik, T., Ricci F., “Distributed Collaborative Filtering with Domain Specialization”, In Proceedings of the 2007 ACM conference on Recommendation systems, Minneapolis, MN, USA, 2007.
[20]Mark O’Connor & Jon Herlocker, “Clustering Items for Collaborative Filtering”, Dept. of Computer Science and Engineering University of Minnesota Minneapolis, MN.
[21]Bamshad Mobasher, HongHua Dai, Tao Luo, Mikinakagawa,, “Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization”, Data Mining and Knowledge Discovery, 6, 61–82, 2002.
[22]SongJie Gong, “A Collaborative Recommender Based on User Information and Item Information”, Proceedings of the 2009 International Symposium on Information Processing (ISIP’09) Huangshan, P. R. China, August 21-23, 2009.
[23]Manos Papagelis, Dimitris Plexousakis, and Themistoklis Kutsuras, “Alleviating the sparsity problem of collaborative filtering using trust inferences”, Trust Management, 2005 – Springer.
[24]Paul te Braak, Noraswaliza Abdullah, Yue Xu, “Improving the performance of collaborative filtering recommender systems through user profile clustering”, 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology – Workshops, 2009.
[25]Maria Laura Clemente, “Experimental Results on Item-based Algorithms for Independent Domain Collaborative Filtering”, International Conference on Automated solutions for Cross Media Content and Multi-channel Distribution, 2008.
[26]SongJie Gong, GuangHua Cheng, “Mining User Interest Change for Improving Collaborative Filtering”, Second International Symposium on Intelligent Information Technology Application, 2008.
[27]John S. Breese, David Heckerman, Carl Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering”, Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, July, 1998.
[28]SongJie Gong, GuangHua Cheng, “An Efficient Collaborative Filtering Algorithm with Item Hierarchy”, Second International Symposium on Intelligent Information Technology Application, 2008.
[29]I-En Liao, Shu-Chuan Liao, Kuo-Fong Kao and Ine-Fei Harn, “A Personal Ontology Model for Library Recommendation System”, Proceedings of 9th Interational Conference on Asian Figital Library, S. Sugimoto et al. (Eds.), Lecture Notes in Computer Science, vol. 4312, Springer-Verlag, November 2006.
[30]Y. Koren, “Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model”, Proceedings 14th ACM Int. Conference on Knowledge Discovery and Data Mining, ACM press, 2008.
[31]研究之實驗資料集提供網站，線上檢索日期：2010年6月，.http://www.grouplens.org/
[32]資料集提供網站，線上檢索日期：2010年6月，http://www.netflix.com/